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- W2593390164 abstract "In this paper, we investigate the effect of transfer of emotion-rich features between source and target networks on classification accuracy and training time in a multimodal setting for vision based emotion recognition. First, we propose emosource-a 6-layer Deep Belief Network (DBN), trained on popular emotion corpora for emotion classification. Second, we propose two 6-layer DBNs - emotarget and emotarget <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>ft</sub> and study the transfer of emotion features between source and target networks in a layer-by-layer fashion. To the best of our knowledge, this is the first research effort to study the transfer of emotion features layer-by-layer in a multimodal setting." @default.
- W2593390164 created "2017-03-16" @default.
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- W2593390164 date "2016-11-01" @default.
- W2593390164 modified "2023-09-25" @default.
- W2593390164 title "Transfer of multimodal emotion features in deep belief networks" @default.
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- W2593390164 doi "https://doi.org/10.1109/acssc.2016.7869079" @default.
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